Informed sub-sampling MCMC: approximate Bayesian inference for large datasets

Files in This Item:
Access to this item has been restricted by the copyright holder until:2019-06-09
File Description SizeFormat 
insight_publication.pdf1.27 MBAdobe PDFDownload    Request a copy
Title: Informed sub-sampling MCMC: approximate Bayesian inference for large datasets
Authors: Maire, Florian
Friel, Nial
Alquier, Pierre
Permanent link: http://hdl.handle.net/10197/10403
Date: 9-Jun-2018
Online since: 2019-05-13T09:14:31Z
Abstract: This paper introduces a framework for speeding up Bayesian inference conducted in presence of large datasets. We design a Markov chain whose transition kernel uses an unknown fraction of fixed size of the available data that is randomly refreshed throughout the algorithm. Inspired by the Approximate Bayesian Computation literature, the subsampling process is guided by the fidelity to the observed data, as measured by summary statistics. The resulting algorithm, Informed Sub-Sampling MCMC, is a generic and flexible approach which, contrary to existing scalable methodologies, preserves the simplicity of the Metropolis–Hastings algorithm. Even though exactness is lost, i.e the chain distribution approximates the posterior, we study and quantify theoretically this bias and show on a diverse set of examples that it yields excellent performances when the computational budget is limited. If available and cheap to compute, we show that setting the summary statistics as the maximum likelihood estimator is supported by theoretical arguments.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Springer
Journal: Statistics and Computing
Volume: 29
Issue: 3
Start page: 449
End page: 482
Copyright (published version): 2018 Springer
Keywords: Bayesian inferenceBig-dataApproximate Bayesian computationNoisy Markov chain Monte Carlo
DOI: 10.1007/s11222-018-9817-3
Language: en
Status of Item: Peer reviewed
Appears in Collections:Insight Research Collection

Show full item record

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.